In contemporary language models, transformer architecture dominates by leveraging self-attention mechanisms for efficient parallel training across large sets of documents. However, transformers and their conventional counterparts like RNNs and CNNs often struggle with efficiency when processing long contexts.
To address this, we introduce ResonatorLM, a novel mechanism that replaces attention with a physics-derived alternative.
ResonatorLM treats token sequences as a single, driven one-dimensional latent field, replacing attention dot products with causal functions of damped resonators. We implemented ResonatorLM on a traditional network architecture and tested it on standard long-context modeling tasks.
Our findings reveal that in a small 6M matched setting, training and prefill speedups increase with sequence length, achieving a decode speed 6.47x compared to a standard optimized transformer at 32K tokens, and accuracy improvements from 55.32% to 61.31% on WikiText.
Blogger's Review: The innovative mechanism of ResonatorLM offers a fresh perspective on long-context processing, demonstrating remarkable performance in both efficiency and accuracy. By incorporating concepts from physics, ResonatorLM not only enhances speed but also achieves breakthroughs in accuracy for long-context modeling, making it a noteworthy advancement in the field.